Introduction to Biostatistics and Machine Learning

 Introduction to biostatistics and machine learning

National course open for PhD students, postdocs, researchers and other employees in need of biostatistical skills within all Swedish universities. The course is geared towards life scientists wanting to be able to understand and use basic statistical and machine learning methods. It would also suit those already applying biostatistical methods but have never got a chance to truly understand the basic statistical concepts, such as the commonly misinterpreted p-value.


Next course

  • April 22th - 26th, 2024
  • Trippelrummet (E10:1307-9), Navet, BMC, Husargatan 3, 751 23 Uppsala


 Application & Registration of interest
  • Application is open
  • If you want to be notified when we organise next course in, autumn 2024 or spring 2025, fill in this form


 Important dates
  • Application deadline: March 8th, 2024
  • Confirmation to accepted students: March 15th, 2024
  • Course days: April 22th - 26th, 2024


 Course content
  • Probability theory
  • Hypothesis testing and confidence intervals
  • Resampling
  • Linear regression methods
  • Introduction to generalized linear models
  • Model evaluation
  • Unsupervised learning incl. clustering and dimension reduction methods
  • Supervised learning incl. classification

More information can be found in last years course.



Preliminary course schedule can be found here.



In this course we focus on an active learning approach. The course participants are expected to do some pre-course reading and exercises, corresponding up to 40h studying. The education consists of teaching blocks alternating between mini-lectures, group discussions, live coding sessions etc.


Entry requirements
  • Basic R programming skills (check your skills by taking our self-assessment test)
      • using R as calculator
      • being able to work with vectors and matrices, incl. subsetting and matrices multiplication 
      • reading in data from .csv files, e.g. with read_csv()
      • printing top few rows or last few rows, e.g. with head() and tail()
      • using in-built summary functions such as sum(), min() or max()
      • being able to use documentation pages for R functions, e.g. with help() or ?()
      • using if else statements, writing simple loops and functions.
      • making simple plots (scatter plots, histograms), both with plot() and ggplot()
      • using tidyverse() for data transformations, e.g. filtering rows, selecting columns, creating new columns etc. 
      • being able to install CRAN packages e.g. with install.packages()
      • being familiar with R Markdown or Quatro format
  • No prior biostatistical knowledge is assumed, only basic math skills (pre-course studying materials will be available upon course acceptance). 
  • BYOL (bring your own laptop) with R and R Studio installed


 Selection criteria
  • Due to limited space the course can accommodate maximum of 25 participants. If we receive more applications, participants will be selected based on several criteria. Selection criteria include correct entry requirements, motivation to attend the course as well as gender and geographical balance.
  • NBIS prioritises academic participants (students, staff, affiliated researchers) in Sweden. We can accept participants from industry and/or outside Sweden if we have seats available and the requirements criteria are met.



3000 SEK

includes lunches and coffee 


travel-main.svg Travel info

For travel information and hotel bookings see Travel Information page 


 Course credits
  • Upon successful course completion, assessed based on active participation in all course session, we will issue a course certificate.

  • Please note that we are not able to provide any formal university credits (högskolepoäng). Many universities, however, recognize the attendance in our courses, and award 1.5 HPs, corresponding to 40h of studying. It is up to participants to clarify and arrange credit transfer with the relevant university department.


 Teaching team
  • Olga Dethlefsen «»
  • Eva Freyhult «»
  • Payam Emami «»
  • Julie Lorent «»
  • Mun-Gwan Hong «»


 Contact us
CC attribution share alike This course content is offered under a CC attribution share alike license. Content in this course can be considered under this license unless otherwise noted.